{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"^DJI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df=df[df['Date']>'2015-12-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>17590.660156</td>\n",
       "      <td>17590.660156</td>\n",
       "      <td>17421.160156</td>\n",
       "      <td>17425.029297</td>\n",
       "      <td>17425.029297</td>\n",
       "      <td>93690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3831</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>17405.480469</td>\n",
       "      <td>17405.480469</td>\n",
       "      <td>16957.630859</td>\n",
       "      <td>17148.939453</td>\n",
       "      <td>17148.939453</td>\n",
       "      <td>148060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3832</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17147.500000</td>\n",
       "      <td>17195.839844</td>\n",
       "      <td>17038.609375</td>\n",
       "      <td>17158.660156</td>\n",
       "      <td>17158.660156</td>\n",
       "      <td>105750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3833</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17154.830078</td>\n",
       "      <td>17154.830078</td>\n",
       "      <td>16817.619141</td>\n",
       "      <td>16906.509766</td>\n",
       "      <td>16906.509766</td>\n",
       "      <td>120250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3834</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>16888.359375</td>\n",
       "      <td>16888.359375</td>\n",
       "      <td>16463.630859</td>\n",
       "      <td>16514.099609</td>\n",
       "      <td>16514.099609</td>\n",
       "      <td>176240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date          Open          High           Low         Close  \\\n",
       "3830  2015-12-31  17590.660156  17590.660156  17421.160156  17425.029297   \n",
       "3831  2016-01-04  17405.480469  17405.480469  16957.630859  17148.939453   \n",
       "3832  2016-01-05  17147.500000  17195.839844  17038.609375  17158.660156   \n",
       "3833  2016-01-06  17154.830078  17154.830078  16817.619141  16906.509766   \n",
       "3834  2016-01-07  16888.359375  16888.359375  16463.630859  16514.099609   \n",
       "\n",
       "         Adj Close     Volume  \n",
       "3830  17425.029297   93690000  \n",
       "3831  17148.939453  148060000  \n",
       "3832  17158.660156  105750000  \n",
       "3833  16906.509766  120250000  \n",
       "3834  16514.099609  176240000  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price=test_df['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = test_df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(697,)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return = df_price.pct_change(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return=daily_return[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3831</td>\n",
       "      <td>-0.015844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3832</td>\n",
       "      <td>0.000567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3833</td>\n",
       "      <td>-0.014695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3834</td>\n",
       "      <td>-0.023211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3835</td>\n",
       "      <td>-0.010152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3836</td>\n",
       "      <td>0.003188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3837</td>\n",
       "      <td>0.007174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3838</td>\n",
       "      <td>-0.022088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3839</td>\n",
       "      <td>0.014094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3840</td>\n",
       "      <td>-0.023870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3841</td>\n",
       "      <td>0.001748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3842</td>\n",
       "      <td>-0.015564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3843</td>\n",
       "      <td>0.007353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3844</td>\n",
       "      <td>0.013274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3845</td>\n",
       "      <td>-0.012942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3846</td>\n",
       "      <td>0.017753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3847</td>\n",
       "      <td>-0.013779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3848</td>\n",
       "      <td>0.007851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3849</td>\n",
       "      <td>0.024684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3850</td>\n",
       "      <td>-0.001040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3851</td>\n",
       "      <td>-0.017973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3852</td>\n",
       "      <td>0.011336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3853</td>\n",
       "      <td>0.004892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3854</td>\n",
       "      <td>-0.012890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3855</td>\n",
       "      <td>-0.010979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3856</td>\n",
       "      <td>-0.000791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3857</td>\n",
       "      <td>-0.006222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3858</td>\n",
       "      <td>-0.015995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3859</td>\n",
       "      <td>0.020029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3860</td>\n",
       "      <td>0.013933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>4497</td>\n",
       "      <td>0.005198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>4498</td>\n",
       "      <td>0.010054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>4499</td>\n",
       "      <td>0.000552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>4500</td>\n",
       "      <td>0.002323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>4501</td>\n",
       "      <td>-0.005269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>4502</td>\n",
       "      <td>-0.000850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>4503</td>\n",
       "      <td>-0.000475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>4504</td>\n",
       "      <td>0.000867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>4505</td>\n",
       "      <td>0.000804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>4506</td>\n",
       "      <td>-0.003052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>4507</td>\n",
       "      <td>-0.002295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>4508</td>\n",
       "      <td>0.004408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4509</td>\n",
       "      <td>0.001073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>4510</td>\n",
       "      <td>0.005657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>4511</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>4512</td>\n",
       "      <td>-0.003539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>4513</td>\n",
       "      <td>0.007092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>4514</td>\n",
       "      <td>0.006050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>4515</td>\n",
       "      <td>0.009514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>4516</td>\n",
       "      <td>0.003246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>4517</td>\n",
       "      <td>-0.006785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>4518</td>\n",
       "      <td>-0.002629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>4519</td>\n",
       "      <td>-0.004036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>4520</td>\n",
       "      <td>0.002071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>4521</td>\n",
       "      <td>0.000695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>4522</td>\n",
       "      <td>0.007291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>4523</td>\n",
       "      <td>0.004605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>4524</td>\n",
       "      <td>0.002034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>4525</td>\n",
       "      <td>-0.007489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>4526</td>\n",
       "      <td>-0.006776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>696 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Adj Close\n",
       "0     3831  -0.015844\n",
       "1     3832   0.000567\n",
       "2     3833  -0.014695\n",
       "3     3834  -0.023211\n",
       "4     3835  -0.010152\n",
       "5     3836   0.003188\n",
       "6     3837   0.007174\n",
       "7     3838  -0.022088\n",
       "8     3839   0.014094\n",
       "9     3840  -0.023870\n",
       "10    3841   0.001748\n",
       "11    3842  -0.015564\n",
       "12    3843   0.007353\n",
       "13    3844   0.013274\n",
       "14    3845  -0.012942\n",
       "15    3846   0.017753\n",
       "16    3847  -0.013779\n",
       "17    3848   0.007851\n",
       "18    3849   0.024684\n",
       "19    3850  -0.001040\n",
       "20    3851  -0.017973\n",
       "21    3852   0.011336\n",
       "22    3853   0.004892\n",
       "23    3854  -0.012890\n",
       "24    3855  -0.010979\n",
       "25    3856  -0.000791\n",
       "26    3857  -0.006222\n",
       "27    3858  -0.015995\n",
       "28    3859   0.020029\n",
       "29    3860   0.013933\n",
       "..     ...        ...\n",
       "666   4497   0.005198\n",
       "667   4498   0.010054\n",
       "668   4499   0.000552\n",
       "669   4500   0.002323\n",
       "670   4501  -0.005269\n",
       "671   4502  -0.000850\n",
       "672   4503  -0.000475\n",
       "673   4504   0.000867\n",
       "674   4505   0.000804\n",
       "675   4506  -0.003052\n",
       "676   4507  -0.002295\n",
       "677   4508   0.004408\n",
       "678   4509   0.001073\n",
       "679   4510   0.005657\n",
       "680   4511   0.000332\n",
       "681   4512  -0.003539\n",
       "682   4513   0.007092\n",
       "683   4514   0.006050\n",
       "684   4515   0.009514\n",
       "685   4516   0.003246\n",
       "686   4517  -0.006785\n",
       "687   4518  -0.002629\n",
       "688   4519  -0.004036\n",
       "689   4520   0.002071\n",
       "690   4521   0.000695\n",
       "691   4522   0.007291\n",
       "692   4523   0.004605\n",
       "693   4524   0.002034\n",
       "694   4525  -0.007489\n",
       "695   4526  -0.006776\n",
       "\n",
       "[696 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0005668398927318741"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_amount = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_amount=initial_amount\n",
    "account_growth=list()\n",
    "account_growth.append(initial_amount)\n",
    "for i in range(len(daily_return)):\n",
    "    total_amount = total_amount * daily_return.iloc[i] + total_amount\n",
    "    account_growth.append(total_amount)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15177.62198859156"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_amount\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9841.55559265112,\n",
       " 9847.134178967573,\n",
       " 9702.428316095129,\n",
       " 9477.229178514592,\n",
       " 9381.017337981924,\n",
       " 9410.928402756417,\n",
       " 9478.446447056207,\n",
       " 9269.086370363075,\n",
       " 9399.725834504,\n",
       " 9175.353341157714,\n",
       " 9191.387433567998,\n",
       " 9048.329254008484,\n",
       " 9114.865414162867,\n",
       " 9235.858081897604,\n",
       " 9116.323109846879,\n",
       " 9278.165444337847,\n",
       " 9150.32031753605,\n",
       " 9222.159328459004,\n",
       " 9449.79804644285,\n",
       " 9439.972471571105,\n",
       " 9270.308682798652,\n",
       " 9375.398960627641,\n",
       " 9421.263975049027,\n",
       " 9299.823518684096,\n",
       " 9197.717565823154,\n",
       " 9190.446460688094,\n",
       " 9133.264548794748,\n",
       " 8987.17552842,\n",
       " 9167.18105418058]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account_growth[1:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd33ad6080>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(account_growth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(696,)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative_return=daily_return.values.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd337b62b0>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cumulative_return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd33748860>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(daily_return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_DJI = pd.read_excel(\"mean_variance_return.xlsx\",\"Sheet2\")\n",
    "return_quarterly = pd.read_excel(\"return_final_quarterly.xlsx\",\"Sheet1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(696,)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(691, 1)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_quarterly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd350549e8>]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_amount=initial_amount\n",
    "account_growth=list()\n",
    "account_growth.append(initial_amount)\n",
    "for i in range(len(df_DJI)):\n",
    "    total_amount = total_amount * df_DJI.iloc[i] + total_amount\n",
    "    account_growth.append(total_amount)\n",
    "plt.plot(account_growth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fd3640e208>]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cumulative_return)\n",
    "plt.plot(return_quarterly.cumsum())\n",
    "plt.plot(df_DJI.cumsum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3832</td>\n",
       "      <td>0.000567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3833</td>\n",
       "      <td>-0.014128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3834</td>\n",
       "      <td>-0.037339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3835</td>\n",
       "      <td>-0.047491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3836</td>\n",
       "      <td>-0.044302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3837</td>\n",
       "      <td>-0.037128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3838</td>\n",
       "      <td>-0.059216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3839</td>\n",
       "      <td>-0.045122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3840</td>\n",
       "      <td>-0.068992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3841</td>\n",
       "      <td>-0.067244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3842</td>\n",
       "      <td>-0.082809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3843</td>\n",
       "      <td>-0.075455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3844</td>\n",
       "      <td>-0.062181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3845</td>\n",
       "      <td>-0.075124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3846</td>\n",
       "      <td>-0.057371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3847</td>\n",
       "      <td>-0.071150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3848</td>\n",
       "      <td>-0.063299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3849</td>\n",
       "      <td>-0.038615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3850</td>\n",
       "      <td>-0.039655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3851</td>\n",
       "      <td>-0.057628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3852</td>\n",
       "      <td>-0.046291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3853</td>\n",
       "      <td>-0.041399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3854</td>\n",
       "      <td>-0.054289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3855</td>\n",
       "      <td>-0.065269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3856</td>\n",
       "      <td>-0.066059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3857</td>\n",
       "      <td>-0.072281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3858</td>\n",
       "      <td>-0.088276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3859</td>\n",
       "      <td>-0.068247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3860</td>\n",
       "      <td>-0.054314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3861</td>\n",
       "      <td>-0.038420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>4497</td>\n",
       "      <td>0.426803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>4498</td>\n",
       "      <td>0.436857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>4499</td>\n",
       "      <td>0.437409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>4500</td>\n",
       "      <td>0.439732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>4501</td>\n",
       "      <td>0.434463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>4502</td>\n",
       "      <td>0.433613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>4503</td>\n",
       "      <td>0.433137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>4504</td>\n",
       "      <td>0.434005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>4505</td>\n",
       "      <td>0.434809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>4506</td>\n",
       "      <td>0.431757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>4507</td>\n",
       "      <td>0.429462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>4508</td>\n",
       "      <td>0.433871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>4509</td>\n",
       "      <td>0.434943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4510</td>\n",
       "      <td>0.440600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>4511</td>\n",
       "      <td>0.440932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>4512</td>\n",
       "      <td>0.437394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>4513</td>\n",
       "      <td>0.444486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>4514</td>\n",
       "      <td>0.450536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>4515</td>\n",
       "      <td>0.460050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>4516</td>\n",
       "      <td>0.463296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>4517</td>\n",
       "      <td>0.456511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>4518</td>\n",
       "      <td>0.453882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>4519</td>\n",
       "      <td>0.449845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>4520</td>\n",
       "      <td>0.451916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>4521</td>\n",
       "      <td>0.452612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>4522</td>\n",
       "      <td>0.459902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>4523</td>\n",
       "      <td>0.464507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>4524</td>\n",
       "      <td>0.466541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>4525</td>\n",
       "      <td>0.459052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>4526</td>\n",
       "      <td>0.452276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>695 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Adj Close\n",
       "0     3832   0.000567\n",
       "1     3833  -0.014128\n",
       "2     3834  -0.037339\n",
       "3     3835  -0.047491\n",
       "4     3836  -0.044302\n",
       "5     3837  -0.037128\n",
       "6     3838  -0.059216\n",
       "7     3839  -0.045122\n",
       "8     3840  -0.068992\n",
       "9     3841  -0.067244\n",
       "10    3842  -0.082809\n",
       "11    3843  -0.075455\n",
       "12    3844  -0.062181\n",
       "13    3845  -0.075124\n",
       "14    3846  -0.057371\n",
       "15    3847  -0.071150\n",
       "16    3848  -0.063299\n",
       "17    3849  -0.038615\n",
       "18    3850  -0.039655\n",
       "19    3851  -0.057628\n",
       "20    3852  -0.046291\n",
       "21    3853  -0.041399\n",
       "22    3854  -0.054289\n",
       "23    3855  -0.065269\n",
       "24    3856  -0.066059\n",
       "25    3857  -0.072281\n",
       "26    3858  -0.088276\n",
       "27    3859  -0.068247\n",
       "28    3860  -0.054314\n",
       "29    3861  -0.038420\n",
       "..     ...        ...\n",
       "665   4497   0.426803\n",
       "666   4498   0.436857\n",
       "667   4499   0.437409\n",
       "668   4500   0.439732\n",
       "669   4501   0.434463\n",
       "670   4502   0.433613\n",
       "671   4503   0.433137\n",
       "672   4504   0.434005\n",
       "673   4505   0.434809\n",
       "674   4506   0.431757\n",
       "675   4507   0.429462\n",
       "676   4508   0.433871\n",
       "677   4509   0.434943\n",
       "678   4510   0.440600\n",
       "679   4511   0.440932\n",
       "680   4512   0.437394\n",
       "681   4513   0.444486\n",
       "682   4514   0.450536\n",
       "683   4515   0.460050\n",
       "684   4516   0.463296\n",
       "685   4517   0.456511\n",
       "686   4518   0.453882\n",
       "687   4519   0.449845\n",
       "688   4520   0.451916\n",
       "689   4521   0.452612\n",
       "690   4522   0.459902\n",
       "691   4523   0.464507\n",
       "692   4524   0.466541\n",
       "693   4525   0.459052\n",
       "694   4526   0.452276\n",
       "\n",
       "[695 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_return.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3832    0.000567\n",
       "3833   -0.014128\n",
       "3834   -0.037339\n",
       "3835   -0.047491\n",
       "3836   -0.044302\n",
       "3837   -0.037128\n",
       "3838   -0.059216\n",
       "3839   -0.045122\n",
       "3840   -0.068992\n",
       "3841   -0.067244\n",
       "3842   -0.082809\n",
       "3843   -0.075455\n",
       "3844   -0.062181\n",
       "3845   -0.075124\n",
       "3846   -0.057371\n",
       "3847   -0.071150\n",
       "3848   -0.063299\n",
       "3849   -0.038615\n",
       "3850   -0.039655\n",
       "3851   -0.057628\n",
       "3852   -0.046291\n",
       "3853   -0.041399\n",
       "3854   -0.054289\n",
       "3855   -0.065269\n",
       "3856   -0.066059\n",
       "3857   -0.072281\n",
       "3858   -0.088276\n",
       "3859   -0.068247\n",
       "3860   -0.054314\n",
       "3861   -0.038420\n",
       "          ...   \n",
       "4497    0.426803\n",
       "4498    0.436857\n",
       "4499    0.437409\n",
       "4500    0.439732\n",
       "4501    0.434463\n",
       "4502    0.433613\n",
       "4503    0.433137\n",
       "4504    0.434005\n",
       "4505    0.434809\n",
       "4506    0.431757\n",
       "4507    0.429462\n",
       "4508    0.433871\n",
       "4509    0.434943\n",
       "4510    0.440600\n",
       "4511    0.440932\n",
       "4512    0.437394\n",
       "4513    0.444486\n",
       "4514    0.450536\n",
       "4515    0.460050\n",
       "4516    0.463296\n",
       "4517    0.456511\n",
       "4518    0.453882\n",
       "4519    0.449845\n",
       "4520    0.451916\n",
       "4521    0.452612\n",
       "4522    0.459902\n",
       "4523    0.464507\n",
       "4524    0.466541\n",
       "4525    0.459052\n",
       "4526    0.452276\n",
       "Name: Adj Close, Length: 695, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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          "2017-03-30",
          "2017-03-31",
          "2017-04-03",
          "2017-04-04",
          "2017-04-05",
          "2017-04-06",
          "2017-04-07",
          "2017-04-10",
          "2017-04-11",
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          "2017-04-13",
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          "2017-04-18",
          "2017-04-19",
          "2017-04-20",
          "2017-04-21",
          "2017-04-24",
          "2017-04-25",
          "2017-04-26",
          "2017-04-27",
          "2017-04-28",
          "2017-05-01",
          "2017-05-02",
          "2017-05-03",
          "2017-05-04",
          "2017-05-05",
          "2017-05-08",
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        }
       ],
       "layout": {
        "title": "Portfolio Value Performance Quarterly with Transaction cost",
        "xaxis": {
         "title": "Quarterly Trade Date"
        },
        "yaxis": {
         "title": "Portfolio Value"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import time\n",
    "from datetime import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "trace0 = go.Scatter(x = test_df['Date'].values, y = cumulative_return, mode = 'lines', name = 'Mean-Variance')\n",
    "trace1 = go.Scatter(x = test_df['Date'].values, y = df_DJI.values.cumsum(), mode = 'lines', name = 'Equally Weighted')\n",
    "\n",
    "\n",
    "data = [trace0, trace1]\n",
    "layout = dict(title = \"Portfolio Value Performance Quarterly with Transaction cost\", \n",
    "              xaxis = dict(title = 'Quarterly Trade Date'), \n",
    "              yaxis = dict(title = 'Portfolio Value'))\n",
    "fig = dict(data = data, layout = layout)\n",
    "\n",
    "plotly.offline.iplot(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'show'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-d533186c7394>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'show'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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